Informatica

Smart card has been adopted to various applications. In 2000, Hwang and Li proposed a remote user authentication scheme, which is also using smart card. Nine months later, Chan and Cheng pointed out that there is a weakness in the remote authentication scheme proposed by Hwang and Li. In this paper, we show that Chan and Cheng's attack does not work well because they did not consider the format of user's identity. In addition, we propose several ways to solve the problem of Chan and Cheng's attack.

In the paper, we present an algorithm that can be applied to protect data before a data mining process takes place. The data mining, a part of the knowledge discovery process, is mainly about building models from data. We address the following question: can we protect the data and still allow the data modelling process to take place? We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data. The presented formal approach is especially useful when the knowledge discovery process is outsourced. The application of the algorithm is demonstrated through an example.

This paper considers the main positions of one‐sided and two‐sided problems. For one‐sided problems only the method of solution “the distance to the ideal point” is discussed in the actual version. For two‐sided problems a distinction is made between games with rational behaviour and games against nature. The main strategic principles are as follows: simple min‐max principle, extended min‐max principle, Wald's rule, Savage criterion, Hurwicz's rule, Laplace's rule, Bayes's rule, Hodges‐Lehmann rule. Questions of transforming the decision‐making matrix are considered. The article gives the description of a software as well as an example of an investment variant estimation.

This paper investigates a class of sharp integral means inequalities involving fractional calculus operators of certain analytic functions with negative coefficients. Some consequences of the main result are also mentioned.

Reinforcement learning addresses the question of how an autonomous agent can learn to choose optimal actions to achieve its goals. Efficient exploration is of fundamental importance for autonomous agents that learn to act. Previous approaches to exploration in reinforcement learning usually address exploration in the case when the environment is fully observable. In contrast, we study the case when the environment is only partially observable. We consider different exploration techniques applied to the learning algorithm “Utile Suffix Memory”, and, in addition, discuss an adaptive fringe depth. Experimental results in a partially observable maze show that exploration techniques have serious impact on performance of learning algorithm.

A quick gradient training algorithm for a specific neural network structure called an extra reduced size lattice‐ladder multilayer perceptron is introduced. Presented derivation of the algorithm utilizes recently found by author simplest way of exact computation of gradients for rotation parameters of lattice‐ladder filter. Developed neural network training algorithm is optimal in terms of minimal number of constants, multiplication and addition operations, while the regularity of the structure is also preserved.

This paper discusses the linear periodically time‐varying (LPTV) system parameter estimation using a block approach. An block algorithm is proposed for optimal estimation of the parameters of LPTV system from the input sequence and the output sequence corrupted by additive Gaussianly distributed noise. In the proposed method, the least squares error criterion has been used.The algorithm provides a useful computational tool based on an appropriate theoretical foundation for parameter estimation of linear time‐invariant (LTI) systems from input and output data. Simulation results are presented that demonstrate the performance of the approach.

Sun's nonrepudiation threshold proxy signature scheme is not secure against the collusion attack. In order to guard against the attack, Hwang et al. proposed another threshold proxy signature scheme. However, a new attack is proposed to work on both Hwang et al.'s and Sun's schemes. By executing this attack, one proxy signer and the original signer can forge any valid proxy signature. Therefore, both Hwang et al.'s scheme and Sun's scheme were insecure.

In an internet environment, such as UNIX, a remote user has to obtain the access right from a server before doing any job. The procedure of obtaining acess right is called a user authentication protocol. User authentication via user memorable password provides convenience without needing any auxiliary devices, such as smart card. A user authentication protocol via username and password should basically withstand the off‐line password guessing attack, the stolen verifier attack, and the DoS attack. Recently, Peyravian and Zunic proposed one password transmission protocol and one password change protocol. Later, Tseng et al. (2001) pointed out that Peyravian and Zunic's protocols can not withstand the off‐line password guessing attack, and therefore proposed an improved protocol to defeat the attack. Independently, Hwang and Yeh also showed that Peyravian and Zunic's protocols suffer from some secury flaws, and an improved protocol was also presented. In this paper, we show that both Peyravian and Zunic's protocols and Tseng et al.'s improved protocol are insecure against the stolen verifier attack. Moreover, we show that all Peyravian and Zunic's, Tseng et al.'s, and Hwang and Yeh's protocols are insecure against DoS attack.

We consider a generalized model of neural network with a fuzziness and chaos. The origin of the fuzzy signals lies in complex biochemical and electrical processes of the synapse and dendrite membrane excitation and the inhibition mechanism. The mathematical operations included into fuzzy neural network modeling are: the scalar product between inputs of layers and synaptic weights is replaced by a fuzzy logic multiplication, the sum of products changes to the fuzzy logic sums, and the operators such as supremum, maximum, and minimum are presented for a fuzzy description. The algorithm of varying membership functions, built basing on a backpropagation paradigm and a method of fuzzy neural optimization, has been considered. Both fuzzy properties and a chaos phenomenon are analyzed basing upon experimental computations.